1. Field of the Invention
The present invention relates to state parameter estimation in a nonlinear system, and more particularly, to a method and apparatus for estimating a state parameter in a nonlinear discrete time system using a geometric data fusion method.
2. Description of the Related Art
To estimate a state parameter in a nonlinear system, an extended Kalman filter has been used as a representative technique. See, Peter S. Maybeck, “Stochastic Models, Estimation and Control, volume 1, chap. 1, pp. 1–14, Academic Press, N.Y., N.Y., 1979, and Welch and Bishop, “An Introduction to the Kalman Filter,” TR 95-041, Dept. of Computer Science, U. of N.C. at Chapel Hill, Feb. 8, 2001, herein incorporated by reference. However, in a method using the extended Kalman filter, if the nonlinearity of a system is great or an initial estimation error is big, the result of a state parameter estimation diverges, and even when the result does not diverge, the estimation performance is not so good.
Also, when the characteristic of a system became known, for example, when the operation range of a state parameter or an output parameter became known, the conventional methods cannot effectively utilize this characteristic of the system to improve the performance of a state parameter estimation.